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"""HashSet dataset."""
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import datasets
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import pandas as pd
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_CITATION = """
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@article{kodali2022hashset,
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title={HashSet--A Dataset For Hashtag Segmentation},
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author={Kodali, Prashant and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam},
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journal={arXiv preprint arXiv:2201.06741},
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year={2022}
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}
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"""
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_DESCRIPTION = """
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Hashset is a new dataset consisiting on 1.9k manually annotated and 3.3M loosely supervised tweets for testing the
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efficiency of hashtag segmentation models. We compare State of The Art Hashtag Segmentation models on Hashset and other
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baseline datasets (STAN and BOUN). We compare and analyse the results across the datasets to argue that HashSet can act
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as a good benchmark for hashtag segmentation tasks.
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HashSet Distant: 3.3M loosely collected camel cased hashtags containing hashtag and their segmentation.
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HashSet Distant Sampled is a sample of 20,000 camel cased hashtags from the HashSet Distant dataset.
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"""
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_URL = "https://raw.githubusercontent.com/prashantkodali/HashSet/master/datasets/hashset/HashSet-Distant-sampled.csv"
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class HashSetDistantSampled(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"index": datasets.Value("int32"),
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"hashtag": datasets.Value("string"),
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"segmentation": datasets.Value("string")
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}
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),
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supervised_keys=None,
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homepage="https://github.com/prashantkodali/HashSet/",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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downloaded_files = dl_manager.download(_URL)
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return [
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files }),
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]
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def _generate_examples(self, filepath):
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records = pd.read_csv(filepath).to_dict("records")
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for idx, row in enumerate(records):
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yield idx, {
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"index": row["Unnamed: 0.1"],
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"hashtag": row["Unsegmented_hashtag"],
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"segmentation": row["Segmented_hashtag"]
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}
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